Addressing Visibility for Encoded Signals

ABSTRACT

The present disclosure relates generally improving visibility artifacts associated with encoded signals. A visibility change for local image areas associated with an encoded signal can be determined through use of a plurality of channel-specific contrast sensitivity functions. Of course, other features, and related claims and combinations are provided as well.

RELATED APPLICATION DATA

This application is a continuation of U.S. application Ser. No.15/214,206, filed Jul. 19, 2016 (U.S. Pat. No. 10,311,538), which is acontinuation of U.S. application Ser. No. 13/664,165, filed Oct. 30,2012 (U.S. Pat. No. 9,396,509), which claims benefit of U.S. ProvisionalApplication No. 61/553,226, filed Oct. 30, 2011.

This application is also related to U.S. patent application Ser. Nos.12/634,505, filed Dec. 9, 2009 (published as US 2010-0150396 A1, nowU.S. Pat. No. 8,199,969) and Ser. No. 12/337,029, filed Dec. 17, 2008(published as US 2010-0150434 A1, now U.S. Pat. No. 9,117,268).

Each of the above patent documents is hereby incorporated by referencein its entirety.

TECHNICAL FIELD

The present disclosure relates generally data hiding, digitalwatermarking and steganography.

BACKGROUND AND SUMMARY

The term “steganography” generally infers data hiding. One form of datahiding includes digital watermarking. Digital watermarking is a processfor modifying media content to embedded a machine-readable (ormachine-detectable) signal or code into the media content. For thepurposes of this application, the data may be modified such that theembedded code or signal is imperceptible or nearly imperceptible to auser, yet may be detected through an automated detection process. Mostcommonly, digital watermarking is applied to media content such asimages, audio signals, and video signals.

Digital watermarking systems may include two primary components: anembedding component that embeds a watermark in media content, and areading component that detects and reads an embedded watermark. Theembedding component (or “embedder” or “encoder”) may embed a watermarkby altering data samples representing the media content in the spatial,temporal or some other domain (e.g., Fourier, Discrete Cosine or Wavelettransform domains). The reading component (or “reader” or “decoder”)analyzes target content to detect whether a watermark is present. Inapplications where the watermark encodes information (e.g., a message orpayload), the reader may extract this information from a detectedwatermark.

A watermark embedding process may convert a message, signal or payloadinto a watermark signal. The embedding process may then combines thewatermark signal with media content and possibly another signals (e.g.,an orientation pattern or synchronization signal) to create watermarkedmedia content. The process of combining the watermark signal with themedia content may be a linear or non-linear function. The watermarksignal may be applied by modulating or altering signal samples in aspatial, temporal or some other transform domain.

A watermark encoder may analyze and selectively adjust media content togive it attributes that correspond to the desired message symbol orsymbols to be encoded. There are many signal attributes that may encodea message symbol, such as a positive or negative polarity of signalsamples or a set of samples, a given parity (odd or even), a givendifference value or polarity of the difference between signal samples(e.g., a difference between selected spatial intensity values ortransform coefficients), a given distance value between watermarks, agiven phase or phase offset between different watermark components, amodulation of the phase of the host signal, a modulation of frequencycoefficients of the host signal, a given frequency pattern, a givenquantizer (e.g., in Quantization Index Modulation) etc.

The present assignee's work in steganography, data hiding, digitalwatermarking and signal detection is reflected, e.g., in U.S. Pat. Nos.:7,072,487; 6,947,571; 6,912,295; 6,891,959; 6,763,123; 6,718,046;6,614,914; 6,590,996; 6,522,769; 6,408,082; 6,122,403 and 5,862,260, andin published specifications WO 9953428 and WO 0007356 (corresponding toU.S. Pat. Nos. 6,449,377 and 6,345,104), and in published U.S. PatentApplication No. US 2008-0298632 A1. Each of the patent documentsmentioned in this paragraph is hereby incorporated by reference in itsentirety. Of course, a great many other approaches are familiar to thoseskilled in the art. The artisan is presumed to be familiar with a fullrange of literature concerning steganography, data hiding and digitalwatermarking.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram corresponding to an image digital watermarkingembedding method.

DETAILED DESCRIPTION

We have found ways to improve signal embedding. An exemplary usescenario operates on a color image or video including a signal encodedtherein. One type of encoding encodes digital watermarking in aplurality of color channels. For example, the color image or video maybe represented in the industry standard luminance and chrominance colorspace called “Lab” (for Lightness (or luminance), plus ‘a’ and ‘b’ colorchannels). Of course, the present disclosure will apply to and work withother color schemes and techniques as well. For example, alternativeluminance and chrominance color schemes include “Yuv” (Y=luma, and ‘u’and ‘v’ represent chrominance channels) and “Ycc” (also a dualchrominance space representation).

In a case where a media signal includes (or may be represented by) atleast two chrominance channels, a watermark embedder may insert the samedigital watermark signal in both the ‘a’ color direction and ‘b’ colordirection. In one example the ‘a’ color direction represents a“blue/yellow” color direction, and the ‘b’ color direction represents a“red/green” color direction. This type embedding can be performed inparallel (if using two or more encoders) or serial (if using oneencoder). The watermark embedder may vary the gain (or signal strength)of the watermark signal in the ‘a’ and ‘b’ channels to achieve improvedhiding of the watermark signal. For example, the ‘a’ channel may have awatermark signal embedded with signal strength (or intensity) that isgreater or less than the watermark signal in the ‘b’ channel. A HumanVisual System response indicates that about twice the watermark signalstrength can be embedded in the blue/yellow channel as the red greenchannel and still achieve favorable (e.g., equalized) visibility.Alternatively, the watermark signal may be embedded with the samestrength in both the ‘a’ and ‘b’ channels. Regardless of the watermarkembedding strength, watermark signal polarity is preferably inverted inthe ‘b’ color plane relative to the ‘a’ color plane. The inverted signalpolarity is represented by a minus (“−”) sign in equation 2.

WMa=a(channel)+wm  (1)

WMb=b(channel)−wm  (2)

WMa is a watermarked ‘a’ channel, WMb is a watermarked ‘b’ channel, andwm represents a watermark signal. A watermarked color image or video(including L and WMb and WMa) can be provided, e.g., for printing,digital transfer or viewing. When printing this type of watermarking innewspaper print the watermark signal is mainly in yellow and magentacolors. Capture, e.g., with a cell phone, of such newspaper printutilizes at least the blue and green channels under white fluorescentlighting.

An encoded signal may include a message or payload having, e.g., a linkto a remote computer resource, metadata or ownership information. Thecolor image or video is rendered (e.g., printed, distributed ordisplayed). A user, e.g., equipped with a camera enabled cell phone,captures an image of an encoded color image or video with her cell phonecamera. The captured image data is analyzed by a signal detector(embedded in the cell phone) to recover the message or payload. Thepresent disclosure provides methods and apparatus to improve thedetection of such encoded signals.

The following section discusses improving signal hiding including usingcontrast sensitivity function and non-iterative embedding.

Visibility Geometry

A contrast sensitivity function (CSF) that describes an impact of addinga single color channel watermark signal image, S, to a color directionof the original image, I, can be defined by,

${\Delta \; {CSF}_{I}} = {\left\lbrack {\left( \frac{\left( {I + S} \right) - {\left( {I + S} \right)*b_{c}}}{\left( {I + S} \right)*b_{c}} \right) - \left( \frac{I - {I*b_{c}}}{I*b_{c}} \right)} \right\rbrack*{HVS}_{I}}$

Where

“*” represents convolution, b_(c) represents the blurring kernel forcontrast, and HVS_(l) represents the HVS (human visual system) responsekernel in the colorspace direction of l. Typically, the image, I, wouldbe one of the three channels from the Lab colorspace representationwhere the HVS kernel corresponds to that channel. In other words, it isthe difference in the contrast between the original and watermarkedimage filtered by the human visual response. Note that the equationsalways describe a value for each pixel in an image.

The signal can be designed to be near zero mean over the region sizedefined by the blurring kernel, b_(c). Therefore, S*b_(c)≅0 and

(I+S)*b _(c) =I*b _(c) +S*b _(c) ≅I*b _(c)

This allows the ΔCSF_(l) equation to be simplified to

${\Delta \; {CSF}_{I}} \cong {\left( \frac{S}{I*b_{c}} \right)*{HVS}_{I}}$

This is an intuitive result since the change in CSF is defined by theratio of local distortion, i.e., watermark signal, to local image meanfiltered by the HVS response. The overall visibility impact, V, of thewatermark signal on the full color image can then given by

$V^{2} = \frac{{\Delta \; {CSF}_{L}^{2}} + {\Delta \; {CSF}_{a}^{2}} + {\Delta \; {CSF}_{b}^{2}}}{1 + {w_{L}{CSF}_{L}^{2}*b_{m}}}$

Where w_(L) is the weighting for luminance contrast masking,

${{CSF}_{L} = {\left( \frac{I - {I*b_{c}}}{I*b_{c}} \right)*{HVS}_{L}}},$

and b_(m) is a luminance masking blurring kernel. We have chosen theabove formula so that the geometry is expressed as an ellipsoid (e.g., aprolated ellipsoid like a squashed ball). Such an ellipsoid my may havea larger radius in the a-b plane and a shorter radius in the L directiondue to the larger impact of L compared to a and b. The optimizationresult will be the same in any monotonic transform of visibility, V.However, the form of V simplifies the optimization.

To easy the discussion, there are no assumptions made about the originalimage other than that it can be transformed into the Lab colorspaceformat. The approach we describe applies to RGB, CMYK, and offset imageswhere specific inks of arbitrary colors are used, among others.

The constant visibility surface in Lab colorspace can be viewed as anaxis-aligned ellipsoid.

The luminance axis of the visibility ellipsoid is typically much smallerthan the chrominance axes, a and b, due to the significantly increasedsensitivity of the HVS to luminance changes over chrominance changes.

Signal Geometry

The constant signal surface can be defined by the detector operation.Assuming a watermark detector that is looking for a signal that isembedded in a single colorspace direction, e.g., gray or “a-b”, theconstant signal surface is a plane perpendicular to the signaldirection. Due to the potential nonlinearity between the colorspace usedby the detector and the Lab colorspace, the signal direction may bedifferent for different regions of colorspace. For simplicity, we assumethat any color transform nonlinearities are small enough so that withinthe normal watermark signal levels, the planar model of constant signalsurface applies. More complex models that can be used to take localnonlinearities into account will not be discussed here.

Optimal Embedding Dominated by Visibility Constraints

If we ignore the constraints due to the color gamut, then theoptimization problem can be simply to find the point on the desiredconstant visibility ellipsoid with the maximum signal projection. Or,stated differently, the point where the positive constant signal planeis tangent to the desired visibility ellipsoid. Note that the directionand magnitude of the maximum point is a direction and weight for thesignal, not a specific signal value. This optimized signal weight ismultiplied by the desired signal to find the specific color value at agiven pixel.

To simplify the form of the equations, rename L, a, and b;

Let the equation of the plane as defined by the signal direction be

p(L,a,b)=p _(L) L+p _(a) a+p _(b) b+p _(d)=0

And the equation of the ellipsoid be

e(L,a,b)=e _(L) L ² +e _(a) a ² +e _(b) b ² =V ²

Then the tangent point in the positive signal direction is

${L = \frac{{kp}_{L}}{e_{L}}},{a = \frac{{kp}_{a}}{e_{a}}},{b = \frac{{kp}_{b}}{e_{b}}}$Where$k = \frac{V}{\sqrt{\frac{p_{L}^{2}}{e_{L}} + \frac{p_{a}^{2}}{e_{a}} + \frac{p_{b}^{2}}{e_{b}}}}$

This leads to an embedding procedure as follows

-   -   1. Transform the original image into Lab colorspace using, e.g.,        ICC profiles (sets of data to represent image information) for        improved accuracy.    -   2. Compute ΔCSF_(l), ΔCSF_(a), ΔCSF_(b), 1+w_(L)CSF_(L) ²*b_(m),        and the local detector signal colorspace direction.    -   3. Solve for L, a, and b signal embedding weights.    -   4. Add optimally weighted signal at each pixel after        transforming weights to embedding colorspace

Number 3, above, can be further simplified when the detector projectionis known to be within the a-b plane. In that case, we look at theelliptic visibility disc in the a-b plane and the direction of thesignal. The solution for a and b (L is assumed to be zero) is then anintersection of a line through origin and ellipse in the a-b plane. Ofcourse, the intersection of any line with an ellipsoid is easily found:(1) write the parametric equation of the line (x=x0+at, y=y0+bt,z=z0+zt), (2) substitute parametric x, y, z expressions into theellipsoid equation, and (3) solve for t.

FIG. 1 is a flow diagram corresponding to an image digital watermarkingembedding method.

Color Gamut Geometry

The overall color gamut is defined by the span of each color in theoriginal image color domain. However, we are primarily interested in thelocal gamut with respect to the level of watermark distortion that wouldreach the visibility limit. For most image regions, we would expect thevisibility ellipsoid to be far from any gamut boundaries. When imageregions are close to gamut boundaries then the visibility ellipsoid mayextend beyond those boundaries. Once our visibility ellipsoid exceeds agamut boundary, we actually encounter two boundaries which are symmetricabout the average image color, e.g., the origin of the visibilityellipsoid.

The symmetric gamut is required because the watermark signal is zeromean and extends equally in the positive and negative directions.Therefore, embedding a signal in a direction away from the gamutboundary will also hit the gamut boundary at the same magnitude as asignal in the opposite direction.

The surface of a color gamut is locally modeled as a plane due to thesmall extent of the gamut surface for watermarking purposes. In general,the gamut surfaces are curved globally due to device characteristicssuch as color element saturation in screens or ink opacity in printdevices. The global gamuts for a target device are preferably known foroptimal watermarking.

In our approach, the planar models of local gamut are transformed intoLab colorspace. Due to the relatively small watermark distortions, thecolor gamut in a local watermark region is expected to be approximatelyplanar in Lab even though the color transform to Lab may be globallynonlinear.

If the maximum signal point on the ellipse is outside the color gamut,then the color gamut planes intersect the ellipse and symmetrically “cutoff” portions of the ellipse containing the maximum signal point. Theconstraint region defined by the color gamut is constructed withpolygons and can be in the shape of a 3D volume (at least four uniquecolors where one color might be the background) or a 2D polygon (atleast three unique colors including background), or a 1D line (at leasttwo colors including background). Note that the 1D case is typically ofan offset situation where a single ink is varied against a substrate toachieve a color range. In all cases, the region formed between thesurfaces of local gamut and visibility constraints represent all of theallowable embedding weights. The optimal embedding weights aredetermined by the point on the constraint region that has the largestsignal projection.

Optimal Embedding Dominated by Gamut Constraints

If we are not willing to modify the average local color, then we cansearch the corners of the gamut region to find the optimal embeddingweights which will result in lower visibility. Since the constant signalsurface is planar, the point where it first meets the gamut region willalways be on a corner (or two corners with equal signal projections). Ifa gamut region corner exceeds the desired visibility, they can bediscarded, otherwise, they can be compared to any intersections betweenthe visibility ellipsoid and the gamut faces or edges.

Optimal Embedding for Intersections between Visibility Gamut Constraints

A more involved case can be where there are intersections between thevisibility and gamut constraints that can be examined. Theseintersections can be in the form of two points if the gamut is a line,and one or more ellipses when the gamut is a plane or volume. There arealso cases for current embedding methods where reasonable approximationscan greatly simplify the intersection computations.

Chrominance Embedding

Chrominance embedding can avoid changes to the luminance channel tominimize visibility. For this type of embedding, the signal direction isin the a-b plane which means that if the maximum signal projection isnot on the ellipsoid and is not a gamut corner, it may lie on the slicethrough the visibility ellipsoid that lies on the a-b plane, i.e., anellipse. Therefore, we can evaluate the intersection between the a-bvisibility ellipse and each of the gamut planes to find possible optimalembedding points.

We can then enumerate the pairs of points for each intersection pair andtest them for gamut limits. We then compare the signal projections ofthe intersection points with any valid gamut corner points to choose thevalid candidate with the largest signal projection.

Gray Embedding

Gray embedding is color agnostic and combines R, G, and B in equal partsfor signal detection. As such, the intersection between a gamut limitplane and the visibility ellipsoid will be an ellipse somewhere on thesurface of the ellipsoid. We would then find the point on eachintersection ellipse that has the largest projection onto the signal andcompare them.

Removing the Gamut Constraints

The solution posed above relies on an exhaustive search for the maximumpoint on a visibility ellipsoid truncated by gamut planes. Although thesearch over the truncated ellipse is straightforward, a simpler approachmay to reshape the colorspace such that the gamut planes no longerintersect with the visibility ellipse. Then the solution reverts to themaximum signal projection on the ellipsoid. Ideally, the colorspacewould be distorted in way that not only moves the gamut planes to thesurface of the ellipsoid, but also minimizes any perceptual differencebetween the original and distorted images. The problem of reducing gamutlimits with minimum perceptual impact has been encountered in manysituations and has been well researched. Fortunately, for the embeddingproblem posed above, we can accurately determine the specific gamutlimits that affect the embedding process as well as the distance incolorspace that the gamut limits are moved to achieve the givenvisibility constraint.

The computing environments used to implement the above processes andsystem components encompass a broad range from general purpose,programmable computing devices to specialized circuitry, and devicesincluding a combination of both. The processes and system components maybe implemented as instructions for computing devices, including generalpurpose processor instructions for a variety of programmable processors,including microprocessors, Digital Signal Processors (DSPs), etc. Theseinstructions may be implemented as software, firmware, etc. Theseinstructions can also be converted to various forms of processorcircuitry, including programmable logic devices, application specificcircuits, including digital, analog and mixed analog/digital circuitry.Execution of the instructions can be distributed among processors and/ormade parallel across processors within a device or across a network ofdevices. Transformation of content signal data may also be distributedamong different processor and memory devices.

The computing devices used for signal detection and embedding mayinclude, e.g., one or more processors, one or more memories (includingcomputer readable media), input devices, output devices, andcommunication among these components (in some cases referred to as abus). For software/firmware, instructions are read from computerreadable media, such as optical, electronic or magnetic storage mediavia a communication bus, interface circuit or network and executed onone or more processors.

The above processing of content signals may include transforming ofthese signals in various physical forms. Images and video (forms ofelectromagnetic waves traveling through physical space and depictingphysical objects) may be captured from physical objects using cameras orother capture equipment, or be generated by a computing device. Whilethese signals are typically processed in electronic and digital form toimplement the components and processes described above, they may also becaptured, processed, transferred and stored in other physical forms,including electronic, optical, magnetic and electromagnetic wave forms.The content signals can be transformed during processing to computesignatures, including various data structure representations of thesignatures as explained above. In turn, the data structure signals inmemory can be transformed for manipulation during searching, sorting,reading, writing and retrieval. The signals can be also transformed forcapture, transfer, storage, and output via display or audio transducer(e.g., speakers).

It will be recognized that this technology finds utility with all mannerof devices—both portable and fixed. PDAs, organizers, portable musicplayers, desktop computers, wearable computers, servers, etc., can allmake use of the principles detailed herein. Particularly contemplatedcell phones include the Apple iPhone, and cell phones following Google'sAndroid specification (e.g., the G1 phone, manufactured for T-Mobile byHTC Corp.). The term “cell phone” should be construed to encompass allsuch devices, even those that are not strictly-speaking cellular, nortelephones.

(Details of the iPhone, including its touch interface, are provided inpublished patent application 20080174570.)

The design of cell phones and other computers that can be employed topractice the methods of the present disclosure are familiar to theartisan. In general terms, each includes one or more processors, one ormore memories (e.g. RAM), storage (e.g., a disk or flash memory), a userinterface (which may include, e.g., a keypad, a TFT LCD or OLED displayscreen, touch or other gesture sensors, a camera or other opticalsensor, a microphone, etc., together with software instructions forproviding a graphical user interface), a battery, and an interface forcommunicating with other devices (which may be wireless, such as GSM,CDMA, W-CDMA, CDMA2000, TDMA, EV-DO, HSDPA, WiFi, WiMax, or Bluetooth,and/or wired, such as through an Ethernet local area network, a T-1internet connection, etc). The processor can be a special purposeelectronic hardware device, or may be implemented by a programmableelectronic device executing software instructions read from a memory orstorage, or by combinations thereof. (The ARM series of CPUs, using a32-bit RISC architecture developed by Arm, Limited, is used in many cellphones.) References to “processor” should thus be understood to refer tofunctionality, rather than any particular form of implementation.

In addition to implementation by dedicated hardware, orsoftware-controlled programmable hardware, the processor can alsocomprise a field programmable gate array, such as the Xilinx Virtexseries device. Alternatively the processor may include one or moreelectronic digital signal processing cores, such as Texas InstrumentsTMS320 series devices.

Software instructions for implementing the detailed functionality can bereadily authored by artisans, from the descriptions provided herein,conclusions, and other determinations noted above.

Typically, devices for practicing the detailed methods include operatingsystem software that provides interfaces to hardware devices and generalpurpose functions, and also include application software that can beselectively invoked to perform particular tasks desired by a user. Knownbrowser software, communications software, and media processing softwarecan be adapted for uses detailed herein. Some embodiments may beimplemented as embedded systems—a special purpose computer system inwhich the operating system software and the application software isindistinguishable to the user (e.g., as is commonly the case in basiccell phones). The functionality detailed in this specification can beimplemented in operating system software, application software and/or asembedded system software.

Different of the functionality can be implemented on different devices.For example, in a system in which a cell phone communicates with aserver at a remote service provider, different tasks can be performedexclusively by one device or the other, or execution can be distributedbetween the devices. Thus, it should be understood that description ofan operation as being performed by a particular device (e.g., a cellphone) is not limiting but exemplary; performance of the operation byanother device (e.g., a remote server), or shared between devices, isalso expressly contemplated. (Moreover, more than two devices maycommonly be employed. E.g., a service provider may refer some tasks,functions or operations, to servers dedicated to such tasks.)

In like fashion, data can be stored anywhere: local device, remotedevice, in the cloud, distributed, etc.

Operations need not be performed exclusively byspecifically-identifiable hardware. Rather, some operations can bereferred out to other services (e.g., cloud computing), which attend totheir execution by still further, generally anonymous, systems. Suchdistributed systems can be large scale (e.g., involving computingresources around the globe), or local (e.g., as when a portable deviceidentifies nearby devices through Bluetooth communication, and involvesone or more of the nearby devices in an operation.)

CONCLUDING REMARKS

Having described and illustrated the principles of the technology withreference to specific implementations, it will be recognized that thetechnology can be implemented in many other, different, forms. Toprovide a comprehensive disclosure without unduly lengthening thespecification, each of the above referenced patent documents is herebyincorporated by reference in its entirety.

The particular combinations of elements and features in theabove-detailed embodiments are exemplary only; the interchanging andsubstitution of these teachings with other teachings in this and theincorporated-by-reference patent documents are also contemplated.

What is claimed is:
 1. An apparatus comprising: memory for storing datarepresenting imagery; means for transforming the data into amulti-channel color space; means for obtaining information indicating animpact of adding a color channel encoded signal to a color direction ofthe data; means for determining a color space direction for encodedsignal detection; means for obtaining weighting factors based on theinformation indicating an impact of adding a color channel encodedsignal; means for weighting the color channel encoded signal withweighting factors to yield a modified color channel encoded signal;means for embedding the modified color channel encoded signal in thedata representing imagery.
 2. The apparatus of claim 1 in which the datarepresenting imagery comprises data representing pixel information, inwhich said means for embedding alters the data representing pixelinformation.
 3. The apparatus of claim 1 in which said means forembedding operates after transforming weights into an embedding colorspace.
 4. The apparatus of claim 1 in which the color channel encodedsignal comprises digital watermarking.
 5. A method comprising: obtainingdata representing imagery; transforming the data representing imageryinto a color space; determining a visibility change for local imageareas associated with an encoded signal through use of a plurality ofchannel-specific contrast sensitivity functions associated with thetransformed data in the color space; determining embedding weights tominimize the visibility change; and applying the determined embeddingweights to pixels in an embedding color space, the embedding weightscollectively conveying the encoded signal.
 6. The method of claim 5 inwhich said transforming utilizes profiles including sets of data torepresent image information.
 7. The method of claim 5 in which one ormore electronic processors are programmed to carry out the method ofclaim
 1. 8. The method of claim 5 in which the data representing imagerycomprises data representing pixel information, in which said applyingcomprises altering the data representing pixel information.
 9. Themethod of claim 5, and prior to said applying, further comprisingtransforming the determined embedding weights into the embedding colorspace.
 10. A non-transitory computer readable medium comprisinginstructions stored thereon to cause a programmed apparatus to performthe method of claim
 5. 11. A non-transitory computer readable mediumcomprising instructions stored thereon to cause a programmed apparatusto perform the method of claim
 8. 12. The method of claim 5 in which theencoded signal comprises digital watermarking.
 13. The method of claim 8in which the encoded signal comprises digital watermarking.